The purpose of this study is to build a system to rationally estimate the agent-based household micro-dataset of the base year for land-use microsimulation. Attributes of a household can be classified into a general set of categories. This system, wherein a Monte Carlo simulation is used, deals with those attributes, either continuous or discrete, in a generalized scheme. It uses sample data that contain full information on the micro-data to establish the correlation between the attributes and the available statistical data or census data. To reproduce the correlation between continuous attribute variables, independent variables that can be obtained based on the sample data are introduced and employed as intervening variables. Attributes of a whole household are probabilistically determined based on Logit and other models obtained from sample data. Finally, a case study of the system application to a person-trip-survey dataset of the Sapporo metropolitan area is presented.

Tourism generation is one of the most important aspects but it remains as an under-researched area in tourism demand forecasting and relevant behavior analysis. This study analyzes individual's decision on whether to go on vacation or not. First, a number of constraints that prohibit tourism participation are explored. Second, individual's choice of tourism participation is studied based on a Scobit model, which includes a skewness parameter to relax the assumption made in binary logit model that the sensitivity of individuals to changes in explanatory variables is highest for those who have indifferent preferences over participation and non-participation. We also introduce the tourism constraint components into the model based on the theoretical consideration in the existing literature. The empirical application is conducted using the data stemmed from a web survey conducted in Japan in 2010. Using this data the impacts of several attributes on participation decisions in tourism are investigated.

The B2C market in Taiwan is obviously becoming a noticeable market. As the market grows and matures, “return” becomes one of the challenges for e-retailers. In the past, most of the literature on return issues focused on the wholesaler-retailer relationship. Recently, due to the advent of Internet-based retailing within the past decade, attention is shifting to the issue of returns in the retailer-consumer relationship. In this study, empirical data is used and decision tree model is implemented to analyze the critical variables revealing the customer return propensity. There are three dimensions of variables in the data set: customer demographic variables, merchandise variables and service variables. Three variables, namely, category, price and delivery days could be used to distinguish customer return propensity more effectively. In accordance with these variables, some strategies for website managers are proposed to control returns in e-retailing.

The traffic conditions in urban areas change with time due to varying congestion levels and incidents, resulting in unexpected variations of travel time on the infrastructure links. These conditions lead to the formulation of the Dynamic Vehicle Routing and scheduling Problem with Soft Time Windows (D-VRPSTW) with dynamic travel time in the city logistics field. This paper presents a column generation-based exact solution approach for the D-VRPSTW. The performance evaluation on a test instance under different dynamic travel time events shows that the D-VRPSTW solutions based on the updated travel times result in significant cost savings as compared to the static version.

This study examines the performance of genetic algorithms (GAs) on solving a pickup and delivery vehicle routing problem, typically faced by home delivery service providers. A number of combinations of GA's three main algorithmic operators, namely, selection, crossover, and mutation is implemented and the data envelopment analysis (DEA) is adopted to evaluate and rank these various combinations of GA operators. The numerical results demonstrate that DEA is appropriate in determining the efficient combinations of GA operators. Among the combinations under consideration, the combination using tournament selection and simple crossover is generally more efficient. The findings also contribute to algorithm development and evaluation of vehicle routing problems from the operations research perspective.

This paper develops a model to determine and to locate the optimal numbers of license plate recognition (LPR) to minimize error rates of O-D matrix estimation, percentages of LPR coverage (a proxy of installation cost), and percentages of recorded trips (a proxy of privacy invasion). A genetic algorithm is proposed to solve the combinatorial problem. To demonstrate the applicability of the proposed model and solving algorithm, two exemplified cases and a real-world case are investigated. The results have consistently showed that the optimal locations of LPR are at both ends and in the middle of the segments of a freeway corridor with heavy link traffics. If an extensive coverage of LPR is attempted, however, additional LPR may be placed at the segments with light link traffics so as to balance out the privacy invasion.

The impact of vehicle emissions on the global climate has drawn increasing concern in the past few decades. Patterns of housing development determine travel behaviors, thus affecting transport-related greenhouse gas emissions. Here, a bi-level model is established to describe the relationships among housing allocation, traffic volume, and CO2 emissions using a continuum modeling approach. The user-equilibrium condition is achieved in the lower-level, and the minimum CO2 emissions are obtained by optimization the housing allocation in the upper-level. A hypothetical city is considered with one central business district (CBD) and a road network that is densely distributed outside of the CBD. Several commuter classes with different values of time are considered. The finite element method, the Newton-Raphson algorithm, and the convex combination approach are applied to solve the constrained optimization problem established in the bi-level model. A numerical example is given to demonstrate the effectiveness of the method.

Demand for trade transport sector is basically a derived demand for tradable goods. On the other hand, the demand for general industries is influenced by the scale of the transport sector in a trade gateway city. Usually trade transport industries are located near a port or an airport. The locational characteristics have to be considered in order to understand the effect of port/airport policy and trade policy on the regional economy. This paper discusses an open economy multi-region computable general equilibrium model which features the activity of trade related industrial sector, with focus on export and import. The behaviors of the export industry and import industry sectors are explicitly formulated in the model as well as other industrial sector. The numerical analysis presents the implications of the impacts of trade demand shock to trade gateway city and hinterland city.